Genetic Algorithms in a slidePremiseEvolution worked once (it produced us!), it might work againBasicsPool of solutionsMate existing solutions to produce new solutionsMutate current solutions for long-term diversityCull population

IntroductionComputing pioneers (especially in AI) looked to natural systems as guiding metaphorsEvolutionary computationAny biologically-motivated computing activity simulating natural evolutionGenetic Algorithms are one form of this activityOriginal goalsFormal study of the phenomenon of adaptationJohn HollandAn optimization tool for engineering problems

Main ideaTake a population of candidate solutions to a given problemUse operators inspired by the mechanisms of natural genetic variationApply selective pressure toward certain propertiesEvolve a more fit solution

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Why evolution as a metaphorAbility to efficiently guide a search through a large solution spaceAbility to adapt solutions to changing environments“Emergent” behavior is the goal“The hoped-for emergent behavior is the design of high-quality solutions to difficult problems and the ability to adapt these solutions in the face of a changing environment” Melanie Mitchell, An Introduction to Genetic Algorithms

GA terminologyIn the spirit – but not the letter – of biologyGA chromosomes are strings of genesEach gene has a number of alleles; i.e., settingsEach chromosome is an encoding of a solution to a problemA population of such chromosomes is operated on by a GA